医学 AI

Patient-specific solution of the electrocorticography forward problem in deforming brain

Benjamin F. Zwick, George C. Bourantas, Saima Safdar, Grand R. Joldes, Damon E. Hyde, Simon K. Warfield, Adam Wittek, Karol Miller

专题命中 临床大模型 ：MRI(abstract)；CT(abstract)

A Predictive Visual Analytics System for Studying Neurodegenerative Disease based on DTI Fiber Tracts

Chaoqing Xu, Tyson Neuroth, Takanori Fujiwara, Ronghua Liang, Kwan-Liu Ma

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.CV、cs.LG、eess.IV

Atrial Fibrillation: A Medical and Technological Review

Samayan Bhattacharya, Sk Shahnawaz

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.CV、cs.LG、eess.SP

Comments 11 pages, 1 figure

Towards BERT-based Automatic ICD Coding: Limitations and Opportunities

Damian Pascual, Sandro Luck, Roger Wattenhofer

专题命中 临床大模型 ：diagnosis(abstract)；biomedical(abstract)

Comments Accepted at BioNLP 2021

COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring

Rula Amer, Maayan Frid-Adar, Ophir Gozes, Jannette Nassar, Hayit Greenspan

专题命中 临床大模型 ：CT(abstract)；分类 cs.CV、cs.LG、eess.IV

Comments paper was accepted to JBHI IEEE journal

Journal ref IEEE J Biomed Health Inform. 2021 Mar 26;PP

Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning

Christina Bukas, Bailiang Jian, Luis F. Rodriguez Venegas, Francesca De Benetti, Sebastian Ruehling, Anjany Sekuboyina, Jens Gempt, Jan S. Kirschke, Marie Piraud, Johannes Oberreuter, Nassir Navab, Thomas Wendler

专题命中 临床大模型 ：CT(abstract)；分类 cs.CV、cs.LG、eess.IV

Comments 7 pages, 5 figures, 5 tables

Attention based Multiple Instance Learning for Classification of Blood Cell Disorders

Ario Sadafi, Asya Makhro, Anna Bogdanova, Nassir Navab, Tingying Peng, Shadi Albarqouni, Carsten Marr

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.CV、cs.LG、eess.IV

Analyzing an Imitation Learning Network for Fundus Image Registration Using a Divide-and-Conquer Approach

Siming Bayer, Xia Zhong, Weilin Fu, Nishant Ravikumar, Andreas Maier

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.CV、cs.LG、eess.IV

Comments 6 pages, 2 figures

Classification of Histopathological Biopsy Images Using Ensemble of Deep Learning Networks

Sara Hosseinzadeh Kassani, Peyman Hosseinzadeh Kassani, Michal J. Wesolowski, Kevin A. Schneider, Ralph Deters

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.CV、cs.LG、eess.IV

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

Xiang Zhang, Lina Yao, Manqing Dong, Zhe Liu, Yu Zhang, Yong Li

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.LG、eess.SP；biomedical(comments)

Comments Accepted by the IEEE Journal of Biomedical and Health Informatics (J-BHI)

Resolving superposition in AI for interpretability and cross-modal alignment in patient-neuronal images

解决AI中的叠加问题以实现可解释性与患者-神经元图像的跨模态对齐

Jisung Park, Seohyeon Kang, Daeun Yoo, Eunsu Lee, Seoin Cho, Wooyeop Choi, Ian Choi, James R. Evan, Daesoo Kim, Sonia Gandhi, Minee L. Choi

机构 * KAIST（韩国科学技术院） ； Konyang University（建阳大学） ； Chang Gung University（长庚大学） ； UCL Queen Square Institute of Neurology & The Francis Crick Institute（伦敦大学学院皇后广场神经病学研究所与弗朗西斯·克里克研究所） 纠错

专题命中 临床大模型 ：pathology(abstract)；分类 cs.CV、cs.LG、q-bio

AI总结 利用稀疏自编码器解决高维生物数据中神经网络表示空间的叠加问题，恢复几何保真度，并通过Gromov-Wasserstein最优传输实现图像与单细胞RNA测序数据的跨模态对齐。

Comments 10 pages, 7 figures (plus 14 in appendix), 1 table, preprint

A Multi-Dimensional Clustering Approach for Identifying Inborn Errors of Immunity

一种多维聚类方法用于识别先天性免疫缺陷

Nishad Kulkarni, Alexandra K. Martinson, Nicholas L. Rider, Michael Keller, Syed Muhammad Anwar

机构 * Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital, Washington, DC（Sheikh Zayed儿童外科创新研究所，儿童医院，华盛顿特区） ； Childrens National Hospital, Washington, DC（儿童医院，华盛顿特区） ； Department of Health Systems & Implementation Science, Division of Allergy & Immunology Virginia Tech Carilion School of Medicine, Roanoke, VA（健康系统与实施科学部门，过敏与免疫学分会弗吉尼亚理工大学Carilion医学院，罗阿诺克，VA） ； Division of Allergy & Immunology Childrens National Hospital, Washington, DC（过敏与免疫学分会儿童医院，华盛顿特区） ； School of Medicine and Health Sciences, George Washington University, Washington, DC（医学与健康科学学院，乔治华盛顿大学，华盛顿特区） 纠错

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.CV、cs.LG、q-bio

AI总结 本文提出一种多维聚类方法，用于从全国数据注册中识别新的罕见疾病模式并提取与先天性免疫缺陷相关的特征，通过改进IEI特征意识和开发罕见疾病人群分析的数据工具包，扩展了复杂医疗记录到可被无监督ML解释的数据结构。

Comments Accepted at EMBC 2026

Conditional Score-Based Diffusion Model for Cortical Thickness Trajectory Prediction

Qing Xiao, Siyeop Yoon, Hui Ren, Matthew Tivnan, Lichao Sun, Quanzheng Li, Tianming Liu, Yu Zhang, Xiang Li

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.LG、q-bio、eess.IV

SynthA1c: Towards Clinically Interpretable Patient Representations for Diabetes Risk Stratification

Michael S. Yao, Allison Chae, Matthew T. MacLean, Anurag Verma, Jeffrey Duda, James Gee, Drew A. Torigian, Daniel Rader, Charles Kahn, Walter R. Witschey, Hersh Sagreiya

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.LG、q-bio、eess.IV

Comments 12 pages. Accepted to PRIME MICCAI 2023

Artificial Intelligence-based Eosinophil Counting in Gastrointestinal Biopsies

Harsh Shah, Thomas Jacob, Amruta Parulekar, Anjali Amarapurkar, Amit Sethi

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.CV、q-bio、eess.IV

Comments 4 pages, 2 figures

Deep Sequential Models for Suicidal Ideation from Multiple Source Data

Ignacio Peis, Pablo M. Olmos, Constanza Vera-Varela, María Luisa Barrigón, Philippe Courtet, Enrique Baca-García, Antonio Artés-Rodríguez

专题命中 临床大模型 ：diagnosis(abstract)；biomedical(comments,journal_ref)；分类 cs.LG

Comments Accepted for publication in IEEE Journal of Biomedical and Health Informatics (JBHI)

Journal ref Journal of Biomedical and Health Informatics, vol.23, no. 6, 2019

The Latent Space Hypothesis: Toward Universal Medical Representation Learning

Salil Patel

机构 * Salil Patel 纠错

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.LG、q-bio；medical AI(comments)

Comments 51 pages, 12 figures. A position paper examining the latent space hypothesis - the proposition that diverse medical data can be represented in shared latent spaces reflecting fundamental biological processes. The paper discusses theoretical foundations, reviews supporting evidence, and considers potential implications for medical AI and representation learning

Translating the future: Image-to-image translation for the prediction of future brain metabolism

Elena Doering, Merle C. Hönig, Tobias Deußer, Gerard N. Bischof, Thilo van Eimeren, Alexander Drzezga, Lotta M. Ellingsen

专题命中 临床大模型 ：diagnosis(abstract)；分类 q-bio、eess.IV；biomedical(journal_ref)

Comments Accepted to SPIE Medical Imaging 2024

Journal ref Medical Imaging 2024: Clinical and Biomedical Imaging

A Robust Deep Learning Approach for Automatic Classification of Seizures Against Non-seizures

X. Yao, X. Li, Q. Ye, Y. Huang, Q. Cheng, G. -Q. Zhang

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.LG、q-bio；biomedical(comments)

Comments 13 pages, 10 figures, submitted to Biomedical Signal Processing and Control

From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks

从手工特征到功能边学习：EEG癫痫检测框架的演变

Sepideh Kheirollahi, Mohammad Rasoul Roshanshah

机构 * School of Electrical and Computer Engineering（电气与计算机工程学院） ； College of Engineering, University of Tehran（塔里哈大学工程学院） 纠错

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.LG、eess.SP

AI总结 本文综述了深度学习在EEG癫痫检测中的局限性，并引入KAN网络作为替代方案，通过可学习的边函数提升可解释性、参数效率和数据稀缺下的鲁棒性。

Markerless Head Tracking for Accurate and Accessible Neuronavigation

无标记头部追踪用于精确且可及的神经导航

Ziye Xie, Oded Schlesinger, Raj Kundu, Jessica Y. Choi, Pablo Iturralde, Dennis A. Turner, Stefan M. Goetz, Guillermo Sapiro, Angel V. Peterchev, J. Matias Di Martino

机构 * Department of Electrical and Computer Engineering, Duke University（电气与计算机工程系，杜克大学） ； Department of Psychiatry & Behavioral Sciences, Duke University（精神病学与行为科学系，杜克大学） ； Duke Institute for Brain Sciences（杜克脑科学研究所） 纠错

专题命中 临床大模型 ：biomedical(abstract)；分类 cs.CV、eess.IV

AI总结 本文提出无标记方法，利用低成本摄像头和算法建模实现高精度神经导航，实验显示其在精度和成本效益上的优势。

Ensemble of Small Classifiers For Imbalanced White Blood Cell Classification

小分类器的集成用于不平衡白血球分类

Siddharth Srivastava, Adam Smith, Scott Brooks, Jack Bacon, Till Bretschneider

机构 * Department of Computer Science, University of Warwick（沃里克大学计算机科学系） ； Warwick Medical School, University of Warwick（沃里克医学院） ； Intelligent Imaging Innovations Ltd（智能影像创新有限公司） 纠错

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.CV、cs.LG

AI总结 本文提出了一种轻量级集成方法，用于白血球分类，通过数据集扩展缓解类别不平衡问题，利用预训练模型实现高效分类。

Comments Accepted at ISBI 2026 WBCBench Challenge

Self-supervised Disentanglement of Disease Effects from Aging in 3D Medical Shapes

在3D医学形状中实现疾病效应与衰老的自监督解耦

Jakaria Rabbi, Nilanjan Ray, Dana Cobzas

机构 * Department of Computing Science, University of Alberta（阿尔伯塔大学计算机科学系） ； Department of Computer Science, MacEwan University（麦克麦大学计算机科学系） 纠错

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.CV、cs.LG

AI总结 本文提出两阶段框架，通过自监督解耦隐式形状表示，解决诊断标签有限时区分疾病与衰老变化的挑战，提升解耦与重建性能。

Comments 10 pages

A XAI-based Framework for Frequency Subband Characterization of Cough Spectrograms in Chronic Respiratory Disease

基于XAI的慢性呼吸疾病咳嗽声频带特征分析框架

Patricia Amado-Caballero, Luis M. San-José-Revuelta, Xinheng Wang, José Ramón Garmendia-Leiza, Carlos Alberola-López, Pablo Casaseca-de-la-Higuera

专题命中 临床大模型 ：biomedical(abstract)；分类 cs.LG、eess.SP

AI总结 本文提出基于XAI的框架，用于分析慢性呼吸疾病咳嗽声的频谱特征，通过分解频带实现可解释的诊断分析。

Comments Updated funder information

GCVAMD: A Modified CausalVAE Model for Causal Age-related Macular Degeneration Risk Factor Detection and Prediction

GCVAMD：一种用于因果性年龄相关黄斑变性的风险因素检测与预测的改进型因果VAE模型

Daeyoung Kim

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.CV、eess.IV

AI总结 本文提出GCVAMD模型，通过改进的因果VAE方法，从原始OCT图像中提取潜在因果因素，用于AMD风险因素的因果推断与预测。

EXR: An Interactive Immersive EHR Visualization in Extended Reality

EXR：一种基于扩展现实的交互式电子健康记录可视化

Benoit Marteau, Shaun Q. Y. Tan, Jieru Li, Andrew Hornback, Yishan Zhong, Shaunna Wang, Christian Lowson, Jason Woloff, Joshua M. Pahys, Steven W. Hwang, Coleman Hilton, May D. Wang

机构 * Georgia Institute of Technology（佐治亚理工学院） ； Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech & Emory University（佐治亚理工学院生物医学工程系及埃默里大学） ； Shriners Hospitals for Children, Headquarters, Tampa, FL（夏普斯儿童医院，总部，佛罗里达州坦帕） ； Shriners Hospitals for Children, Philadelphia, PA（夏普斯儿童医院，费城，宾夕法尼亚州） 纠错

专题命中 临床大模型 ：CT(abstract)；分类 cs.CV、cs.LG

AI总结 本文提出了一种基于扩展现实的EHR可视化平台，整合结构化和非结构化患者数据，支持沉浸式交互和实时协作，为下一代临床决策支持工具奠定基础。

Comments 11 pages, 6 figures. Preprint version. This paper has been accepted to IEEE ICIR 2025. This is the author-prepared version and not the final published version. The final version will appear in IEEE Xplo

Stable-Drift: A Patient-Aware Latent Drift Replay Method for Stabilizing Representations in Continual Learning

Stable-Drift: 一种患者感知的潜在漂移重放方法，用于持续学习中稳定表示

Paraskevi-Antonia Theofilou, Anuhya Thota, Stefanos Kollias, Mamatha Thota

机构 * National Technical University of Athens（希腊国家技术大学） ； London School of Economics and Political Science（伦敦政治经济学院） ； University of Lincoln（林肯大学） 纠错

专题命中 临床大模型 ：CT(abstract)；分类 cs.CV、cs.LG

AI总结 Stable-Drift方法通过患者感知的潜在漂移重放，有效减少持续学习中的灾难性遗忘，提升医学影像任务的鲁棒性。

Comments 8 pages, 2 figures

Journal ref Proceedings of the IEEE/CVF International Conference on Computer Vision, 2025, 7340--7349

Personalization on a Budget: Minimally-Labeled Continual Learning for Resource-Efficient Seizure Detection

Amirhossein Shahbazinia, Jonathan Dan, Jose A. Miranda, Giovanni Ansaloni, David Atienza

机构 * Embedded Systems Laboratory, EPFL（EPFL嵌入式系统实验室） 纠错

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.LG、eess.SP

Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification

Nathan Painchaud, Jérémie Stym-Popper, Pierre-Yves Courand, Nicolas Thome, Pierre-Marc Jodoin, Nicolas Duchateau, Olivier Bernard

专题命中 临床大模型 ：pathology(abstract)；分类 cs.CV、cs.LG

Comments 13 pages + 2 pages of supplementary material, accepted for publication in IEEE TUFFC

Surface EMG Profiling in Parkinson's Disease: Advancing Severity Assessment with GCN-SVM

Daniel Cieślak, Barbara Szyca, Weronika Bajko, Liwia Florkiewicz, Kinga Grzęda, Mariusz Kaczmarek, Helena Kamieniecka, Hubert Lis, Weronika Matwiejuk, Anna Prus, Michalina Razik, Inga Rozumowicz, Wiktoria Ziembakowska

专题命中 临床大模型 ：diagnosis(abstract)；分类 cs.LG、eess.SP

Comments International Conference on Hybrid Artificial Intelligence Systems (HAIS 2024)